M. Zarándi

1.3k citations
25 papers · 1.1k indexed · h-index 16

M. Zarándi

25 papers receiving 1.1k citations

Peers

M. Zarándi
Comparison fields: 5 of 90
  • Physiology 421
  • Molecular Biology 386
  • Endocrinology, Diabetes and Metabolism 290
  • Cellular and Molecular Neuroscience 255
  • Pharmacology 178
Replace Katherine A. B. Kellett with:
Katherine A. B. Kellett United Kingdom
Ágnes Kenessey United States
Natalia V. Koudinova Russia
Claus T. Christoffersen Denmark
Evelyn J. Perez United States
Masafumi Fujimoto Japan
Kenji Asakura Japan
Liqin Zhao United States
Angela M. Bodles United States
Xiangdong Xu China
M. Zarándi relative to Katherine A. B. Kellett United Kingdom Katherine A. B. Kellett's profile →
Citations per field
00.5×1.6×
Katherine A. B. Kellett · 1×
Citations per year

Countries citing papers authored by M. Zarándi

Since Specialization
Citations

This map shows the geographic impact of M. Zarándi's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by M. Zarándi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M. Zarándi more than expected).

Fields of papers citing papers by M. Zarándi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by M. Zarándi. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by M. Zarándi. The network helps show where M. Zarándi may publish in the future.

Co-authorship network of co-authors of M. Zarándi

This figure shows the co-authorship network connecting the top 25 collaborators of M. Zarándi. A scholar is included among the top collaborators of M. Zarándi based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with M. Zarándi. M. Zarándi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 26
2 1
3 20
4 62
5
Basic HGF-like peptides inhibit generation of liver metastases in murine and human tumor models.
5
6 223
7 107
8 66
9 38
10 30
11
beta-Amyloid[1-40]-induced early hyperpolarization in M26-1F cells, an immortalized rat striatal cell line.
1
12 43
13 56
14 22
15 84
16 10
17 90
18 2
19
Inhibition of electroshock-induced seizures by cholecystokinin-related peptides in mice.
4
20 24

About M. Zarándi

M. Zarándi is a scholar working on Endocrinology, Diabetes and Metabolism, Cellular and Molecular Neuroscience and Physiology, having authored 25 papers that have together received 1.1k indexed citations. Recurring topics across this work include Alzheimer's disease research and treatments (10 papers), Growth Hormone and Insulin-like Growth Factors (9 papers) and Neuroscience and Neuropharmacology Research (5 papers). The work is most often cited by research in Endocrinology, Diabetes and Metabolism (290 citations), Physiology (421 citations) and Cellular and Molecular Neuroscience (255 citations). M. Zarándi has collaborated with scholars based in Hungary, United States and Netherlands. Frequent co-authors include Botond Penke, Andrew V. Schally, Kate Groot, György B. Halmos, Tibor Harkany, Csaba Nyakas, István M. Ábrahám, Csaba Kónya, Patricia Armatis and Katalin Soós. Their work appears in journals such as Proceedings of the National Academy of Sciences, JNCI Journal of the National Cancer Institute and Biochemical and Biophysical Research Communications.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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